Interest in screening for ovarian cancer is growing. Five-year survival in women diagnosed with ovarian cancer is 50% overall, but in women with cancer confined to the ovaries, it is 95%. Only 25% of ovarian cancer is diagnosed in this early stage, however, suggesting that there is an opportunity for significant improvement through early detection. Our goal is to improve the accuracy of a previously developed microsimulation model of ovarian cancer screening by accounting for heterogeneity in the disease and in the population screened, in order to evaluate the cost-effectiveness of using a marker panel longitudinally to detect developing disease. We will expand the scope of the model to accommodate use of 1) a panel of serum markers for screening, and 2) risk-based screening. We will also incorporate QOL effects of both screening and disease, and update the model with respect to screening and treatment costs. These efforts will enable us to identify the potentially most efficient strategies for ovarian cancer screening and to report their cost-effectiveness. The specific aims of this study are twofold: one, to develop a state-of-the-art microsimulation model of ovarian cancer screening and two, to use the model to explore the cost-effectiveness of alternative strategies for ovarian cancer screening. There are two components to aim 2: to identify potentially cost-effective strategies for ovarian cancer screening using a panel of serum markers and imaging and to estimate the cost-effectiveness of the strategies in various populations defined by risk level. Two randomized controlled trials (RCT) of ovarian cancer screening are underway, one in the U.S. and one in the U.K., but results will not be available for several more years. Regardless of the outcomes of the RCTs, questions about cost-effectiveness, the efficacy of more frequent screening, and innovative use of multiple markers and imaging will remain. Molecular discoveries are likely soon to yield a panel of markers that can be used together as a first-line screen in a multimodal strategy involving imaging. Because it would be prohibitively expensive to conduct new RCT to test each potentially better screening strategy, an accurate simulation model will be necessary to develop sensible health care policy as well as to direct future research at both the basic and applied level. To improve the accuracy of the model's predictions, we will refine it to account for heterogeneity in the disease (histology, grade) and heterogeneity in the population screened (risk level). In addition, we will refine the detection component of the model to better represent imaging, and update the cost estimates used in the model, using insurance claims data from Regence Blue Cross of Washington State and Medicare. Extensive validation will be undertaken to assess the consistency of the model's predictions with estimates obtained from trials. Ultimately, an enhanced microsimulation model that incorporates new developments, innovations of the future as well as those we recognize today, will help guide policy and research investment decisions.